This projects studies statistical learning machines as applied to biomedical and clinical prediction, probabilitiy assignment, regresssion, and ranking problems. The algorithms involved include Random Forests, support vector machines, neural networks, and variations of the boosting algorithm. These are all recently developed techniques orginally constructed by the machine learning community, and which are only now starting to see applications in biomedical problems. As the methods were not designed through statistical reasoning or routinely applied to data collected by clinicians or biomedical researchers, these new techniques require modifications and enhancements appropriate to data collected from these alternate sources. In particular, we address the problem of (1) greatly unbalanced data sets, where the researcher typically has only a handful of positive cases and a great many negative cases, (2) the issue of accurate estimates of prediction error rates, where the researcher typically has a relatively small data set upon which to do both model fitting and testing, and (3) the interpretation of the means by which the prediction engine operates and the development of practical prognostic factors. These three problems are essential questions facing the use of modern prediction engines, but have been only lightly studied by the machine learning community. We have applied these statistical learning machine methods to a wide variety of biological datasets. At the invitaion of Cambridge University Press we are in the process of writing a textbook on the subject of "Statistical Learning for Biological Data".